DagSemProc.04401.5.pdf
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Information-Based Nonlinear Approximation: An Average Case Setting
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Dagstuhl Seminar Proceedings, Volume 4401
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2005-04-19
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Leszek Plaskota. Information-Based Nonlinear Approximation: An Average Case Setting. In Algorithms and Complexity for Continuous Problems. Dagstuhl Seminar Proceedings, Volume 4401, p. 1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)
https://doi.org/10.4230/DagSemProc.04401.5
Abstract
Nonlinear approximation has usually been studied under deterministic assumption and complete information about the underlying functions. We assume only partial information and we are interested in the average case error and complexity of approximation. It turns out that the problem can be essentially split into two independent problems related to average case nonlinear (restricted) approximation from complete information, and average case unrestricted approximation from partial information. The results are then applied to average case piecewise polynomial approximation, and to average case approximation of real sequences.
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- average case setting
- nonlinear approximation
- information-based comlexity
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